In conventional retail stores, when objects such as apples, strawberries, lettuce or other produce are purchased, the object(s) must be manually identified by a checkstand operator who then enters identifying information into a point-of-sale machine to complete a transaction. This manual process requires the checkstand operator to be proficient in identifying the different types of items that can be purchased, such as the various types of produce. Because the process is manual and depends upon the skill and training of the checkstand operator, the process is error prone and slow, even if specially trained personnel perform the manual identification task. Moreover, special training needed by checkstand operators to recognize the various different objects to be purchased can be expensive and time consuming.
One technique that has been developed to assist checkstand operators in the identification process is the placing of labels with a numerical identification code on the produce items prior to their purchase. The checkstand operator manually enters the code into the point-of-sale machine at the time of purchase. This technique has the disadvantage of requiring the identifying labels with numerical codes to be applied to the produce items at some point prior to sale, which can be costly with regard to both printing of the labels and labor to apply them to the produce items. Further, some products are difficult to label or cannot be labelled. In addition, this technique still requires manual entry of the numerical codes by the checkstand operator, which is slow and prone to errors.
To reduce the reliance on manual identification of codes and the labor intensive placement of labels on produce, automatic produce and grocery item recognition systems have been proposed. For example, one such proposed system utilizes a color video camera to detect visual cues and analyzes certain visual characteristics such as color, texture, shape and size in an attempt to determine enough "uniqueness" of the product to identify it. Such systems have yet to prove feasible in handling the variable characteristics in like items, and the changing characteristics of produce items as they age or ripen.
It would therefore be advantageous to provide a system for automatic recognition of items, such as produce items to be purchased at retail stores. It would further be advantageous to provide such a system that accurately identifies items despite variance in visual characteristics between like items, and despite potentially changing characteristics of produce items as they age or ripen.